Quick-commerce apparel retail operates in an environment characterized by uncertain demand, short replenishment windows, and high service-level expectations. Traditional inventory planning methods used by many apparel brands are largely manual and reactive, often resulting in stock imbalances, stock outs, and excess inventory holding. This research develops a demand-driven inventory planning framework for quick-commerce apparel operations using historical sales analysis, PID-level product segmentation, and structured replenishment logic. The framework integrates normalization-based product prioritization with inventory control parameters such as reorder point (ROP), safety stock, and order-up-to level. Key performance indicators including Cycle Service Level (CSL), Fill Rate (FR), and Days of Stock on Hand (DS SOH) were used to evaluate the effectiveness of the proposed model. A pilot implementation demonstrated improvements in product availability and inventory alignment across high-performing products. The study highlights the importance of a data-driven inventory planning system for improving operational efficiency in fast-paced quick-commerce environments.